6D Object Pose Tracking - For setups where object CAD
models are available, significant progress has been made in 6D pose tracking. This includes techniques based on hand-crafted probabilistic filtering [1]}, [2]}, [3]}, optimization [4]}, [5]}, [6]}, [7]}, and machine learning [8]}, [9]}. The requirements, however, of such instance-level 3D models, either for training offline or model-frame registration during tracking, complicate generalization to novel instances. More recently, a 6D pose tracking approach [10]} relaxed the assumption to category-level 3D models using 3D object CAD model databases for training [11]}. During testing, the target object category needs to be identified and the corresponding network for that category is utilized for tracking. Instead of being limited to the number of categories such database is able to include, this work employs deep features that in principle can be trained on arbitrary 2D images. It allows generalization to diverse novel objects, as shown in the accompanying experiments.
